Questions tagged [multimodality]

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Sampling for multimodal posterior using Metropolis-Hastings

I was wondering how the well-known Metropolis-Hastings algorithm changes when sampling from a multi-modal posterior. In particular I was wondering what would change in the acceptance ratio in order to ...
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how can we apply masked language modelling on the images using multimodal models?

It might not be clear from the question what I want to say, but how can we apply masked language modelling with the text and image given using multimodal models like lxmert. For example, if there is ...
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Validity of assumptions to compare groups with different distributions

My experiments result in four different groups of data. Here is one example: I wanted to compare the means and variances of these groups. I learnt that a test such as Welch's ANOVA with a Games-...
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Conducting Regression on a Multimodal Dependent Variable

The past two weeks I have been trying to tackle this complex problem where I have the following distribution of my dependent variable (log scale X-Axis). Using Sklearn, I've tried to tackle this using ...
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Lecture notes for the dip test

Are there any good lecture notes/textbook/exposition article that explains the Hartigans' dip test of unimodality? I have seen the original paper but I was wondering if there are any other learning ...
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Is there a way to calculate peaks of a distribution mathematically?

I’m trying to find some way to mathematically calculate the number of peaks in a distribution. I know there are various tests (such as Shapiro Wilk) that assess whether a distribution is normal, but I’...
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Propagating losses in multimodal data model with joint fusion

I am building a model that combines imaging and structured data together. We plan on using a joint fusion architecture where we first process the images into nx1 vector using a CNN. We'll then combine ...
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Compare multimodal distributions for different groups

I am analyzing data from 3 different gait speeds. For each group/speed, I am determining specific value called "angle". Each group has different sample size. So, I need to compare multimodal ...
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How to test for inequality in the presence of non-independent noise?

I have multiple samples which include a response time of a system. I want to test if no sample is significantly different (primarily the expected value). For two sample testing I'm using the sign test ...
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Comparing means of multimodal distributions

I have a game in which players need to accomplish a certain set of tasks, and will receive a number of points for each task. If a player fails a task, he gets 0 points for that task. If he ...
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Can a bayesian neural network with independent normal variational distributions over weights and biases produce a multi-modal posterior?

For a project I am involved in, I am doing surrogate modelling. This means that I simulate data $\mathcal{D}=\{X,Y\}$ that is used to train some probabilistic non-linear regression model. The model is ...
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Measures of central tendency and dispersion of multimodal distributions

I want to make the argument that the mean is not an optimal description of the central tendency of a multimodal distribution and neither is the standard deviation an optimal measure of the dispersion ...
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Statistic for measuring the magnitude of bimodality in a distribution?

Here are some distributions of US political views by industry: After observing their mostly bimodal nature, I would like to measure the degree of bimodality in each of the distributions for the ...
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Testing for uniformity of p-values with multi-modal samples

I'm working with data that is multi-modal, I need to be able to check if the individual samples are statistically distinct or not, so I'm running KS-test against pairs of samples. But I've noticed ...
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Is the multimodal as in multimodal machine learning the same as that as in multimodal distribution?

The multimodal distribution is a distribution with multiple modes as shown below. It reminds me of the multimodal machine learning where multimodal implies multiple types of information, just like ...
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What is a robust distribution for truncated, multi-modal count data for use in GLM analysis?

I have a dataset consisting of observations of number of fish caught per sampling event and would like to conduct a variety GLM analyses on it using R. The maximum number of fish is capped at 75 (...
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Random Forest performed significantly better than other models for Multimodal data, Why?

Sorry about the vague question. I have multimodal human biosensory data, from eyes, body position and EEG. In my classification, Random Forest performed better than Neural Networks, SVM or Naive ...
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Finding the first tail in distribution [closed]

Pdf of a distribution is shown in the figure below. Is there a way to estimate the first tail (or) to segment first mode and its tail? he KDE plot resulted from a transaction dataset where ...
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Does the multimodal probability distribution tend toward uniform distribution as number of modes becomes very large?

Does the multimodal probability distribution tend toward uniform distribution as number of modes becomes very large? Multimodal probability density distribution is formed by the convex combination of ...
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Significance of modes in a distribution

I have several datasets with angular measurements, i.e. circular values from 0 to $2\pi$. These datasets tend to have peaks at 0 and/or $\pi$, and I need to tell if the peaks are detected/significant. ...
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Multi-View Survival Analysis

I have a data set containing various subsets of medical data about a cohort of patients. For example there are blood test results, demographics, medical examination results and a medical history among ...
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Is this Bayesian model averaging?

A classical example of Bayesian model averaging (BMA) is the regression setup where the choice of different sets of covariates corresponds to different models $\mathcal{M}_k$, $k = 1, \ldots, K$, ...
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Which statistical methods are best suited for distribution with two peaks?

My data shows this distribution: I am looking for a statistical distribution which my data follows. Thought about poisson distribution, but goodness of fit test shows p < 0.05
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Fastest Multimodal sampler

I am currently working with Multinest, a bayesian multimodal sampler however it becomes slow for higher dimensions, exponentially slow. Is there another sampler out there that can give me parameter ...
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2 votes
1 answer
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Confusion about multimodal machine learning

I recently browsed through this tutorial on multimodal data. Attention: Multimodal in the sense of feature of very different type, that express the same thing -think picture and voice of ...
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Is the Latent Dirichlet Allocation topic posterior multimodal?

In fitting the Latent Dirichlet Allocation with collapsed Gibbs sampling one builds a sampled approximation to the topic posterior distribution, $P(z|w)$ and use that to calculate the topic and word ...
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Multimodality of mixtures of more than two Normal distributions

Let $$\phi(x;\mu,\sigma) = \frac{1}{\sigma \sqrt{2\pi}} \exp \left(- \frac{(x-\mu)^2}{2\sigma^2}\right)$$ denote the Gaussian density function ($\sigma > 0$). Let $$f(x) = \sum_{i=1}^N p_i \...
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How to combine multiple signal data in my ML model?

I'm doing a task where I need to work with healthcare data from a few different sources. For example, one is an audio signal recording while another is biometric signal reading such as ECG. Both of ...
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How to numerically find the mode of a joint probability distribution from samples? [duplicate]

I have a large number of samples (say $N$) from a multimodal joint probability distribution, for example: ...
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Does it make sense to calculate MLE for multimodal distributions?

The simplest examples of multimodal distributions I've seen are mixtures, namely mixtures of normals. However, in this case, the Maximum Likelihood Estimator [MLE] doesn't make much sense. An example ...
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Distinguish between underlying Distribution and data shape in data transforming?

My question is not well worded, which is part of the problem. I’m specifically trying to apply this to my understanding of Six Sigma, but it probably applies everywhere. I know that having a normal ...
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inverse integration of multimodal distribution

I have a probability distribution, with a number of modes with different peak values, and I have to capture the 90% most significant value ranges. My idea is to apply a threshold starting from the ...
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Is redundancy across different modalities required in multimodal machine learning

There are lots of articles available pertaining to 'multi-modal machine learning'. Among the major challenges, there is a one of representation i.e. "how to represent and summarize multi-modal data ...
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Evaluation of MCMC samples

My model contains five parameters. I want to make Bayesian estimation, but the Bayes estimates can not be obtained in closed form. So, I used Metropolis-Hastings to generate MCMC samples from ...
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mean variance of multimodal distribution

This may be too much of a simplistic question: but is it correct to say that it simply doesn't make sense to compute averages/means of data that is fundamentally multimodal? That is, there is not one ...
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Multimodality from unimodal variables

Let's say a data matrix $\bf{X} \in \mathbb{R}^{N \times D}$ has $D$ random variables each with $N$ observations. So $j$th column of $\bf{X}$ is $N$ observations of $j$th random variable. Suppose ...
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Detecting if an 1-dimenisional distribution is Multimodal

I'm writing up some C++ code for one of my Master's coursework. What I'm actually doing at the moment isn't on the syllabus, but I wish to implement it anyway as it will allow me to produce my own ...
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3 votes
2 answers
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Is the likelihood of the sum of unimodal likelihoods also unimodal?

Let $p$ be a probability distribution and let $\mathcal{D}_1$, $\mathcal{D}_2$ be two sets of observations. If the likelihood of the parameter for some observations $$ \mathcal{L}(\theta; \mathcal{D})...
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How to detect multivariate binomial distributions?

I tried the hartigans dip test, and it works well for univariate distributions. However, when i tried taking each variable (dimension) and applied hartigans dip test (assuming that if along one ...
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4 votes
2 answers
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Multimodal prior

In Bayesian method, a posterior can be either unimodal or multimodal. But, I cannot find any multimodal prior case yet. I wonder if it is possible, and there is any case that is using multimodal ...
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Describe why a distribution might be multimodal?

Suppose that I visualized a distirbution and noticed that it was multimodal. For example, I collected the heights of a bunch of students. I notice that the distirbution is multimodal. How would I ...
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